Supplementary Figure 7: Analysis of detection limit for each cell subset in LM22
From: Robust enumeration of cell subsets from tissue expression profiles
(a) Same as in Supplementary Fig. 6, except here detection limit was assessed using defined inputs of naïve B-cells added to simulated mixtures of the remaining 21 cell types from LM22 (Supplementary Table 1). The impact of unknown content on detection limit was evaluated by adding simulated GEPs created by randomly permuting naïve B-cell genes. Data are presented as medians (n = 4 mixtures) ± 95% confidence intervals. (b) Same as in a, but for all cell types in LM22. To prevent higher magnitude spike-ins from driving the correlation, we summarized performance using the non-parametric Spearman rank correlation, and compared known and predicted fractions over all spike-ins and levels of unknown content tested. Considering these results in aggregate, CIBERSORT significantly outperformed other methods tested (P < 0.0001; paired two-sided Wilcoxon signed rank test; n = 22 cell subsets). Of note, CIBERSORT also outperformed other methods in relation to linear fit, as measured by Pearson correlation. For further details, see Online Methods.
